The importance of data depends on its ability to influence marketing decisions. Through traditional and digital data along with Big Data, organizations can get a vast amount of information e.g. on market trends and transitions as well as customer segments. This information helps organizations make strategic decisions. (Kumar et al., 2013.) Data needs to be turned into information that can be used for actionable business tactics.
Therefore, data mining using statistical methods is needed for processing and analyzing the large quantities of data. To gain the most effective insights, organizations need to combine both traditional market research and data mining. Market research provides insights on a macro level, but data mining helps to identify hidden information that can-not be obtained through traditional methods. (Chiu & Tavella, 2008, pp. 6, 139.)
One important aspect of data use is that in order to succeed, organizations need to cre-ate full portraits of their customers instead of just a series of snapshots. This means that organizations need to gather and combine all the data from their customers together, from basic customer data to transactions and browsing history. (Van Bommel et al., 2014.) Also, a big challenge is how to combine the acquired data with marketing strategy. Or-ganizations can improve branding by using customer data about their buying behavior, and then increasing engagement with more personalized marketing. In addition, organ-izations can use Big Data analytics for evaluating which marketing tactics work and which not and adjust the strategy accordingly. (Jiménez-Zarco, 2019.)
Digital data and Big Data have a strong influence on segmentation and targeting, and they have enabled the personalization of marketing communications. These areas of marketing are therefore explored further in the following subsections.
3.2.1 Segmentation and Profiling
The traditional way to start with marketing is segmentation, i.e. categorizing consumers into groups based on different kinds of factors like demographics, psychographics, and
behavioral profiles (Kotler et al., 2017, pp. 47). Profiling, on the other hand, means cre-ating unique descriptions of the discovered segments. Understanding the audiences is important for effective advertising and communication. Segmentation can be considered as one of the most important analytics, because it enables the organization to identify groups of people who share similar interests and to match the right products, messages, and incentives to the right audiences. Segmentation needs to be objective-driven to be effective, and the objectives can be e.g. to understand audience behavior, needs, or val-ues. Moreover, data can be used to profile individual customers in a way that finds the most suitable products and services targeted to the customer. (Chiu & Tavella, 2008, pp.
140, 195; Fan et al., 2015.) McDonald (2011, pp. 30) notes that in addition to specifying target customers, correct market information enables organizations to effectively meas-ure market share and growth, recognize relevant competition, and formulate a market strategy. However, Fan et al (2015) state that segmentation is becoming increasingly challenging because of Big Data and the volume and variety of data.
3.2.2 Targeting and Personalization
Data has been used for a long time for segmentation and targeting purposes. However, Big Data has increased the effectiveness of data use by enabling real-time personaliza-tion. Big Data is especially useful in retailing, as the industry has access to a large variety of data from online purchases, social media conversations, and location data from mo-bile devices. Individual consumer’s behavior can be tracked and modeled in real-time to recognize when a customer is about the make a purchase decision. The customer can then be nudged into completing a purchase with bundling products and offering reward program savings. (Brown et al., 2011.)
The difference between targeting and personalization is that targeting aims to reach spe-cific groups of consumers, whereas personalization aims to reach individual consumers.
Targeting is based on consumer segments and profiling, and personalization utilizes per-sonal data like name, address, and email address. Moreover, perper-sonalization is mostly used in an organization’s own media like newsletters, whereas targeting is usually used
in paid media like website banners and social media advertisements. (Strycharz & Smit, 2019.) The benefits of personalization for marketers include improved customer satis-faction and customer loyalty, making it more difficult for competitors to lure the custom-ers away. (Chellappa & Sin, 2005).
The techniques that use personalization include online behavioral targeting, email mar-keting, social media advertising, applications and notifications, on-site personalization, customization, and price differentiation. Online behavioral targeting means that an indi-vidual’s online behavior is stored in cookies, i.e. text files stored on PCs and mobile de-vices. In addition, users’ data from social media sites can be used for advertising. Behav-ioral data can be used to find out which topics are interesting to an individual and adver-tising can then be adjusted accordingly. Online behavioral targeting is employed using automatic algorithms, and with these algorithms, personal and contextual data rises to be central in personalization. In email marketing, personalization is a standard process.
It is employed by including information about the recipient, such as adding the person’s name in the greeting or sending special offers on the customer’s birthday. In addition, emails can be personalized in terms of content, based on demographic or behavioral data. The aim of this kind of personalization is to make communication more meaningful.
Personalization of emails is an effective method as it increases the open rates of emails.
(Strycharz & Smit, 2019.)
Social media advertising is mostly based on general targeting, as personalization activi-ties in social media generates more negative reactance from consumers. Personalization of content in mobile applications is not widely used because of users’ privacy concerns.
Still, in-app personalization is more accepted by consumers than personalization of no-tifications. On-site personalization means that a website’s look, feel, and content can be adjusted according to the individual’s personal preferences. This type of personalization is often too costly, and therefore personalization is mainly used only in landing pages from emails. Customization is also sometimes used, meaning that individuals can for ex-ample adapt a website to their own personal needs by using filters. Personalization is
then self-driven by the customers instead of being automated by the organization. Finally, the price of an online product or service can be personalized based on information about an individual consumer. (Strycharz & Smit, 2019.)
Cloud computing has enabled effective personalized filtering, which means that infor-mation can be filtered by different criteria for individual users. The system can then pre-dict what kind of information is relevant to the user and will be filtered out. Google, for example, uses previous search words, location, and social media data to customize search results. Facebook uses personalized filtering based on how users interact with each other. Other services like Amazon and Netflix are also based on heavy personalized filtering, and in the future, personalized filtering it is bound to increase even more. (Boz-dag & Timmermans, 2001.) In addition, location data can be seen as an important source of personalized marketing information. Mobile technology enables organizations to uti-lize location-based services to personauti-lize communication to specific locations at specific times. In advertising, location can be used to deliver advertisements or product recom-mendations based on the user’s current or predicted location. (Fan et al., 2015.)